Joanna Horabik

Compliance for uncertain inventories via probabilistic/fuzzy comparison of alternatives

A direct comparison among highly uncertain inventories of emissions is inadequate and may lead to paradoxes. This issue is of particular importance in the case of greenhouse gases. This paper reviews the methods for the comparison of uncertain inventories in the context of compliance checking. The problem is treated as a comparison of uncertain alternatives. It provides a categorization and ranking of the inventories which can induce compliance checking conditions. Two groups of techniques to compare uncertain estimates are considered in the paper: probabilistic and fuzzy approaches. They show certain similarities which are revealed and stressed throughout the paper. The group of methods most suitable for the compliance purpose is distinguished. They introduce new conditions for fulfilling compliance, depending on inventory uncertainty. These new conditions considerably change the present approach, where only the reported values of inventories are accounted for.

The Cramér-Rao Lower Bound for the Estimated Parameters in a Spatial Disaggregation Model for Areal Data

Consider the problem of allocation of spatially correlated gridded data to finer spatial scale, conditionally on covariate information observable in a fine grid. Spatial dependence is captured with the conditional autoregressive structure, suitable for gridded (areal) data. In this study, we give suitable formulae for the (expected and observed) Fisher information matrix, which is useful for obtaining the Cramer-Rao lower bound of the estimated model parameters. All required derivatives are evaluated analytically, and no numerical differentiation is involved.

A Market for Pollution Emission Permits with Low Accuracy of Emission Estimates

Uncertainties of pollution inventories are often high due to low precision of emission quantity assessments for many emitting sources. A good example is emission of greenhouse gases, where uncertainty of some sources may be as high as 40-100%, while uncertainty of other sources is as low as 2-3%. This discrepancy in uncertainty should be accounted for in compliance as well as in emissions trading, because the traded commodities have different quality. The compliance and emissions trading rules have been discussed in earlier papers by the present authors [14, 15, 16, 17]. In this chapter we focus on presentation of the idea of a market for emissions with so highly scattered uncertainties.

Pricing of uncertain certified emission reductions in a Chinese coal mine methane project with an extended Rubinstein-Ståhl model

The development of coal mine methane (CMM) projects is subject to various kinds of risk, one of these being their highly variable methane content. In this study, a new methodology is proposed to reflect the impact of this uncertainty on a negotiated Certified Emission Reduction (CER) price, which is based on the available information. To simulate a process of price negotiation the Rubinstein-Ståhl bargaining game is utilized, where a buyer’s discount factor is unknown. It is assumed that a buyer’s willingness to accomplish price negotiations depends on the CER uncertainty. The bargaining model has been extended by introducing dependence of its three parameters on the probability of a failure to fulfil the contracted CER amount. To quantify this probability, we develop a conditional distribution given information on the point estimate of methane amount for the project under consideration, and on the distribution of available estimates from coal mines having similar characteristics. The proposed approach is applied to a particular CMM capture and utilization project in Anhui province, China. The results indicate that the uncertainty influence is significant, particularly when the credibility of a seller increases, i.e. the probability of a failure to fulfil the project decreases. The analysis can be of use to both negotiating parties at an early stage of a comprehensive CMM project planning.